Streaming based machine learning predictions are very useful when it comes to high traffic and democratizing predictions of models in organizations.

Let’s examine a use case where a streaming based machine learning system is more efficient compared to real-time (synchronous) predictions (RESTful in most cases) and batch predictions.

You’re leading the recommenders system team at a e-commerce company and your goal is to develop a model that will power product recommendation carousels in many parts of the website using the same model. After some data analysis, you concluded that recent user behaviour is crucial signal for delivering high quality product…

I’m an AWS ML Hero and I was thinking how Reinforcement Learning and AWS can help to make the distribution of Covid-19 test kits in a more effective and efficient manner. Why? Identifying as many people who are infected as possible looks to be one of the key things to minimize or stop the spread of Covid-19. This looks to be one of the reasons why South Korea managed to flatten the curve.


So, let’s define first what Multi-Armed-Bandits (MABs) are. First of all it’s a Reinforcement Learning methodology. You’re faced repeatedly with the problem of choosing among k different…

Do you remember the days at university studying AI and Reinforcement Learning theories before the last decade’s AI boom? YES! :-)

I’m pretty sure that you were also exposed to some toy examples, such as finding your way in a Maze or the Atari Pong game. Then, you asked yourself “Amazing! I can solve real problems now!” So, you turned on your laptop but you realized that you were missing some important parts:

  1. Are there simulators/environments (such as open AI Gym) to mimic a real-world scenario?
  2. Are there mature RL libraries out there to develop RL algorithms and test them…

As a Data Scientist or Machine Learning engineer, I’m sure you have faced the following situation: you are working on a Machine Learning or Deep Learning project, and you need to train and deploy your models in production. But you had enough creating your own recipe scripts to train the models on the cloud (AWS, Google Cloud, etc) or waiting for ages to train the models locally on your workstation.

This is why you started using AWS SageMaker! However, it is still not straightforward to deploy your own training/prediction code on SageMaker. You know, AWS is like IKEA! They provide…

Pavlos Mitsoulis

Staff Data Scientist @ Expedia Group, AWS ML Hero and Co-Creator of Kenza & Sagify #machinelearning, #deeplearning #softwareengineering

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